use numpy.bool_

Author: Vahid Tavanashad

dpnp/dpnp_iface_nanfunctions.py

@@ -987,7 +987,7 @@ def nanstd(
     ddof : {int, float}, optional
         Means Delta Degrees of Freedom. The divisor used in calculations
         is ``N - ddof``, where ``N`` the number of non-NaN elements.
-        Default: `0.0`.
+        Default: ``0.0``.
     keepdims : {None, bool}, optional
         If ``True``, the reduced axes (dimensions) are included in the result
         as singleton dimensions, so that the returned array remains
@@ -1087,7 +1087,7 @@ def nanvar(
     ddof : {int, float}, optional
         Means Delta Degrees of Freedom.  The divisor used in calculations
         is ``N - ddof``, where ``N`` represents the number of non-NaN elements.
-        Default: `0.0`.
+        Default: ``0.0``.
     keepdims : {None, bool}, optional
         If ``True``, the reduced axes (dimensions) are included in the result
         as singleton dimensions, so that the returned array remains

dpnp/tests/test_umath.py

@@ -476,7 +476,7 @@ def test_values(self, dt):
 
     @pytest.mark.parametrize(
         "dt",
-        [numpy.bool, numpy.int32, numpy.int64, numpy.float32, numpy.float64],
+        [numpy.bool_, numpy.int32, numpy.int64, numpy.float32, numpy.float64],
     )
     def test_range(self, dt):
         a = numpy.array([1000000, -1000000, 1000200, -1000200], dtype=dt)